An empirical study of semi-supervised structured conditional models for dependency parsing
Tipus de documentText en actes de congrés
Data publicació2009
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
Abstract
This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach. We describe an extension of semisupervised structured conditional models (SS-SCMs) to the dependency parsing problem, whose framework is originally proposed in (Suzuki and Isozaki, 2008). Moreover, we introduce two extensions related to dependency parsing: The first extension is to combine SS-SCMs with another semi-supervised approach, described in (Koo et al., 2008). The second extension is to apply the approach to secondorder parsing models, such as those described in (Carreras, 2007), using a twostage semi-supervised learning approach. We demonstrate the effectiveness of our proposed methods on dependency parsing experiments using two widely used test collections: the Penn Treebank for English, and the Prague Dependency Treebank for Czech. Our best results on test data in the above datasets achieve 93.79% parent-prediction accuracy for English, and 88.05% for Czech.
CitacióSuzuki, J. [et al.]. An empirical study of semi-supervised structured conditional models for dependency parsing. A: Conference on Empirical Methods in Natural Language Processing. "Conference on Empirical Methods in Natural Language Processing 2009". Singapur: 2009, p. 551-560.
Versió de l'editorhttp://aclweb.org/anthology-new/D/D09/D09-1058.pdf
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
suzukietal-emnlp09.pdf | An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing, EMNLP-2009 | 615,5Kb | Visualitza/Obre |